Discovery Model¶

Next, we will assess the discovery model, where we can perform parameter estimation of values in a PDE system. This is commonly referred to as “inverse modeling,” as opposed to forward modeling (or inference) where we provide all the input information to the f_model and solve the whole solution via a neural network function approximation, forcing the residual term of the f_model constrained output to 0.0 via an MSE loss.

This case is almost a more natural application of neural networks, as it is a physics-constrained methodology of supervised learning. We have data output from a simulation or an experiment, and we seek to map parameters of a known PDE system to that data. In this case, the neural network works via a multi-faceted loss function, where the residual of the f_network is being forced to 0.0 via an MSE loss. However, the key difference is that we are also using the estimated parameters to find that loss. Therefore, we also take the loss wrt the target variables (the estimated parameters, in this case) and optimize those terms via an MSE loss against the training data. The result is a training methodology that not only trains the \(u(\textbf{X}, t)\) solution (or just \(u(\textbf{X})\), depending on your problem), but also estimates parameters within the model concurrently.

In this section we will dig into the DiscoveryMode() in TensorDiffEq. We will discuss the pertinent methods, as well as list a few examples to get you off the ground in training your models.

Initialize¶

DiscoveryModel()

The DiscoveryModel class initialized without any arguments.

Methods¶

The DiscoveryModel class has methods to pass in the data for the problem, as well as methods to fit and predict similar to the CollocationModelND() class.

Compiling the Model¶

First we must feed in the data and fitting parameters using the compile method.

compile(layer_sizes, f_model, X, u_star, var, col_weights=None)

Args:

layer_sizes - a list of ints describing the width and depth of your MLP network used for approximation. See here for more information
f_model - a func describing the physics model. The f_model for a DiscoveryModel must contain the input variable vars_ as the second input, before the input variables to the PDE system. See the example here. The definition of the variables themselves will be in the var input
X - a list of [N,1] arrays of input data, one for each variable (i.e. a list of [x,t] where x and t are [N,1] arrays)
u_star - an array containing the exact solution data, with at point to point correlation to the data input to X
var - a list of tf.Variables, preinitialized, for training the parameters you are interested in in your model
col_weights - a tf.Variable array of size [N,1] for collocation weights for each data point in the experimental data

Once compiled, the model is ready to begin training the parameters in var to fit the data presented in the [X, u_star] pairs. As mentioned above, this training is performed concurrently to training a \(u(\textbf{X}, t)\) network for the problem presented in f_model.

Fitting the model¶

Once compiled, we can fit the model.

fit(tf_itter)

Args:

tf_iter - the number of iterations of training to be completed by the optimizer. Optimizer defaults to tf.Keras.Optimizers.Adam(.005) but can be modified.